Contrast Driven Color-Group Assignment in Categorical Data Visualization

Éric Languenou

2023

Abstract

Ubiquitous digital technology has facilitated the collect of multi-dimensional numerical data that are analyzed by specialists. Their need to explore and to explain this data to non-specialists is important. With categorical data, we construct various diagrams on a color-coded paradigm associating colors with data classes. Depending on the number of classes or the geometry of diagrams, the class-color assignment choice can become a complicated task, with the number of permutations growing in a factorial way with the number of categories. The goal of this research is to develop an algorithm aiming at assigning the best color, among a user given color palette, for each class of objects of a categorical data visualization. We optimize the ability, for a viewer, to distinguish classes’ geometrical objects one from another using a concept of contrast importance factors expressing the need to get for a pair of objects classes a high color contrast. The method relies on a fitness function separation between palette color distances and geometrical contrast need. We indicate applications of the concept to two kinds of categorical visualizations: streamgraphs and chord diagrams for which optimized color assignment has never been published so far.

Paper Citation

in Harvard Style

Languenou É. (2023). Contrast Driven Color-Group Assignment in Categorical Data Visualization. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP; ISBN 978-989-758-634-7, SciTePress, pages 53-64. DOI: 10.5220/0011615000003417

in Bibtex Style

@conference{ivapp23,
author={Éric Languenou},
title={Contrast Driven Color-Group Assignment in Categorical Data Visualization},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP},
year={2023},
pages={53-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011615000003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP
TI - Contrast Driven Color-Group Assignment in Categorical Data Visualization
SN - 978-989-758-634-7
AU - Languenou É.
PY - 2023
SP - 53
EP - 64
DO - 10.5220/0011615000003417
PB - SciTePress